When learning Bayesian statistics, I feel like its common to see some version of the plot that shows a prior density, a density of the likelihood, and a posterior density, somewhere in between (e.g. https://goo.gl/images/Ksg7uR or
I was trying to build of version of this graph for one of the parameters in my model - using RStan. Getting the prior curve is quite easy (since the prior is just normal with a specific mean/sd, plotting it with
stat_function), and the posterior is pretty easy (just extract the sample, and plot it with
stat_density) - but is there a way to get the likelihood density? Is there something I’d need to put into
generated quantities or something like that?
My first attempt was to just run a new model, with an extremely uninformative prior (like
normal(0, 100), and assume that the posterior of that represents the likelihood of the data, but I’m getting a nonsensical result, where the posterior is higher than either the prior or the likelihood.
Is there a way to do this? Thanks!